Since everyone seems to be all twitterpated about Oracle Database 12c this week, I thought I’d post a quick note to let you know that the slides from the presentation on 12c Adaptive Optimization I did at the Hotsos Symposium 2013 (with a lot of help from Maria) are now available in the Whitepapers / Presentations section of this blog.

That’s the section that talks about the Adaptive Optimization stuff. I think the documentation folks meant that they were describing performance features that didn’t require any manual intervention, but it sort of reads like the features are really easy to describe, or maybe that the writers weren’t going to work very hard on describing them. At any rate, the wording struck me as humorous. :)

In Part 1 of this series I talked about the basic problem, which is that plan_hash_values are not based on the whole plan. One of the main missing components is the predicates associated with a plan, but there are other missing parts as was pointed out in Part 1 of Randolf Geist’s post on the topic. At any rate, predicates seem to be the most critical of the missing parts.

The purpose of this second post on the topic is to talk about diagnosis. Basically how do you identify when some other part of a plan has changed that doesn’t affect the plan_hash_value, specifically a predicate.

So first I thought I would show a few examples of statements with the same sql_id and plan_hash_value that have other plan differences (in the predicate section). To do this I used a method proposed by Randolf Geist a few years back in his 2nd post on the topic which covered Alternative Ways to Calculate a PLAN_HASH_VALUE In that post, Randolf shows several ways to compute a hash value on any or all of the columns in the v$sql_plan table. I wrote a simple script around one of the those methods (find_pred_mismatch.sql), and as you might guess from the name, I limited this version to not include all the columns in v$sql_plan, but to only identify statements with mismatched predicates. To be more explicit, the script will locate statements in the shared_pool that have multiple child cursors, where there are more than one set of predicates to go with a single plan_hash_value. Here’s an example:

I know no one really likes the term “tuning” these days, but it’s a short catchy word that gets the idea across. So I’ll just stick with it for the title of this post.

Note that this is one of those posts that’s not really supposed to be about how to solve a particular problem. It’s really just a story about a distraction that I ran into and I how I thought about getting around the issue and then ultimately resolving the root cause. Maybe you will find it instructive to see the process.

So I have this script that I use occasionally (paramon.sql) to see what parallel query slaves are doing. Unfortunately the script doesn’t have a header in it, but I’m pretty sure I lifted it from Randolf Geist. I can’t find it on his blog anywhere, but it looks like his style of writing SQL, and PX Query is something he’s written a lot about, so I’m pretty sure that’s where I got it. (Update: see Jonathan Lewis’s comment below attributing the script to Andy Brooker) Anyway, the script has worked great for me in the past but I recently noticed that it was really sluggish on a couple of 11gR2 DB’s running on Exadata. Here’s an example:

I had an interesting little project this morning. Of course it takes longer to write it down than to do actually do it, but it was kind of interesting and since I haven’t done a post in quite some time (and it’s the day before Thanksgiving, so it’s pretty quite at the office anyway) I decided to share. One of the Enkitec guys (Tim Fox) was doing a performance comparison between various platforms (Exadata using it’s IB Storage Network, Oracle Database Appliance (ODA) using it’s direct attached storage, and a standard database on a Dell box using EMC fiber channel attached storage). The general test idea was simple – see how the platforms stacked up for a query that required a full scan of a large table. More specifically, what Tim wanted to see was the relative speed at which the various storage platforms could return data. The expectation was that the direct attached storage would be fastest and the fibre channel storage would be slowest (especially since we only had a single 2G HBA). He tested ODA and Exadata and got basically what he expected, but when he went to test the database on the Dell he was surprised that it was actually faster than either of the other two tests. So here’s some output from the initial tests: First the Exadata. It’s an X2 quarter rack with one extra storage server. Note that we had to set cell_offload_processing to false to turn off the Exadata storage optimizations, thus giving us a measurement of the hardware capabilities without the Exadata offloading.

I modified my create_1_hint_sql_profile.sql script (which I blogged about here: Single Hint Profiles) to allow any arbitrary text sting including quote characters. This is a script that I use fairly often to apply a hint to a single SQL statement that is executing in a production system where we can’t touch the code for some reason. For example, it’s sometimes useful to add a MONITOR hint or a GATHER_PLAN_STATISTICS hint to a statement that’s behaving badly so we can get more information about what the optimizer is thinking. I recently updated the script to allow special characters in the hint syntax. This feature is useful when you want to add something like an OPT_PARAM hint that takes quoted arguments. The change makes use of the q-Quote feature which I blogged about here: q-Quote. (the original version just barfed on quotes being input as part of the hint)

In the example I turned off cell offload processing with the ALTER SESSION and ran a SQL statement that took 28 seconds. Then I used my fsx.sql script to verify that the statement was not offloaded and to find the SQL_ID. Next I created a 1 hint Profile with an OPT_PARAM hint that set the cell_offload_processing parameter back to TRUE using the new version of my create_1_hint_sql_profile.sql script. Next I used my sql_profile_hints.sql script to verify the text of the hint that was added to the Profile. It looked good including the quotes. When I executed the statement a second time it ran in 5 seconds. I then used fsx.sql again to see that the statement was offloaded for the second execution (child 1).